Identification of Learning Javanese Script Handwriting Using Histogram Chain Code

Abstract

One of the wealth of the Indonesian nation is the many tribes with their own languages and scripts. One of the scripts that has existed since long before the independence of the State of Indonesia is the Javanese script, with the use of Latin script used by almost every aspect of life, both official official activities and daily use, the use of traditional scripts, especially Javanese script, is increasingly scarce. To facilitate learning the Javanese script, learning media is needed with the ability to recognize Javanese characters. In this study, pre-processing was used, especially feature extraction using the Histogram Chain Code (HCC) method and classification using artificial neural networks using the Multi Layer Perceptron method. This study compares four research models by setting the number of HCC feature extraction parameters obtained from one intact image and 3 divided images of 4, 9 and 16 parts respectively so that the total parameters of each HCC model are 8, 32, 72 and 128 parameters characteristic. The training and testing process using the Multi Layer Perceptron method uses 2000 handwritten Javanese script image data which is divided into 80%, namely 1600 images for the training process and 400 images for the testing process. This research resulted in different accuracies, namely 57%, 78%, 83% and 76%. The best accuracy is obtained from the HCC model with 72 parameters and the image is divided into 9 sections.